arXiv subjects

Luca Luceri

Publications and source records attributed to Luca Luceri.

At least 37 records · Page 2Linked to original sources

arXiv2025

A longitudinal analysis of misinformation, polarization and toxicity on Bluesky after its public launch

Bluesky is a decentralized, Twitter-like social media platform that has rapidly gained popularity. Following an invite-only phase, it officially opened to the public on February 6th, 2024, leading to a significant expansion of its user base. In this paper, we present a longitudinal analysis of user activity in the two months surrounding its public launch, examining how the platform evolved due to this rapid growth. Our analysis reveals that Bluesky exhibits an activity distribution comparable to more established social platforms, yet it features a higher volume of original content relative to reshared posts and maintains low toxicity levels. We further investigate the political leanings of its user base, misinformation dynamics, and engagement in harmful conversations. Our findings indicate that Bluesky users predominantly lean left politically and tend to share high-credibility sources. After the platform's public launch, an influx of new users, particularly those posting in English and Japanese, contributed to a surge in activity. Among them, several accounts displayed suspicious behaviors, such as mass-following users and sharing content from low-credibility news sources. Some of these accounts have already been flagged as spam or suspended, suggesting that Bluesky's moderation efforts have been effective.

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arXiv2025

Exposing Cross-Platform Coordinated Inauthentic Activity in the Run-Up to the 2024 U.S. Election

Coordinated information operations remain a persistent challenge on social media, despite platform efforts to curb them. While previous research has primarily focused on identifying these operations within individual platforms, this study shows that coordination frequently transcends platform boundaries. Leveraging newly collected data of online conversations related to the 2024 U.S. Election across $\mathbb{X}$ (formerly, Twitter), Facebook, and Telegram, we construct similarity networks to detect coordinated communities exhibiting suspicious sharing behaviors within and across platforms. Proposing an advanced coordination detection model, we reveal evidence of potential foreign interference, with Russian-affiliated media being systematically promoted across Telegram and $\mathbb{X}$. Our analysis also uncovers substantial intra- and cross-platform coordinated inauthentic activity, driving the spread of highly partisan, low-credibility, and conspiratorial content. These findings highlight the urgent need for regulatory measures that extend beyond individual platforms to effectively address the growing challenge of cross-platform coordinated influence campaigns.

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arXiv2025

Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election

Approximately 50% of tweets in X's user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: What political content are users exposed to beyond their established networks, and what implications does this have for democratic discourse online? In this paper, we present a six-week audit of X's algorithmic content recommendations during the 2024 U.S. Presidential Election by deploying 120 sock-puppet monitoring accounts to capture tweets from their personalized "For You" timelines. Our objective is to quantify out-of-network content exposure for right- and left-leaning user profiles and assess any potential inequalities and biases in political exposure. Our findings indicate that X's algorithm skews exposure toward a few high-popularity accounts across all users, with right-leaning users experiencing the highest level of exposure inequality. Both left- and right-leaning users encounter amplified exposure to accounts aligned with their own political views and reduced exposure to opposing viewpoints. Additionally, we observe that new accounts experience a right-leaning bias in exposure within their default timelines. Our work contributes to understanding how content recommendation systems may induce and reinforce biases while exacerbating vulnerabilities among politically polarized user groups. We underscore the importance of transparency-aware algorithms in addressing critical issues such as safeguarding election integrity and fostering a more informed digital public sphere.

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arXiv2025

IOHunter: Graph Foundation Model to Uncover Online Information Operations

Social media platforms have become vital spaces for public discourse, serving as modern agoràs where a wide range of voices influence societal narratives. However, their open nature also makes them vulnerable to exploitation by malicious actors, including state-sponsored entities, who can conduct information operations (IOs) to manipulate public opinion. The spread of misinformation, false news, and misleading claims threatens democratic processes and societal cohesion, making it crucial to develop methods for the timely detection of inauthentic activity to protect the integrity of online discourse. In this work, we introduce a methodology designed to identify users orchestrating information operations, a.k.a. IO drivers, across various influence campaigns. Our framework, named IOHunter, leverages the combined strengths of Language Models and Graph Neural Networks to improve generalization in supervised, scarcely-supervised, and cross-IO contexts. Our approach achieves state-of-the-art performance across multiple sets of IOs originating from six countries, significantly surpassing existing approaches. This research marks a step toward developing Graph Foundation Models specifically tailored for the task of IO detection on social media platforms.

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arXiv2025

Beyond Interaction Patterns: Assessing Claims of Coordinated Inter-State Information Operations on Twitter/X

Social media platforms have become key tools for coordinated influence operations, enabling state actors to manipulate public opinion through strategic, collective actions. While previous research has suggested collaboration between states, such research failed to leverage state-of-the-art coordination indicators or control datasets. In this study, we investigate inter-state coordination by analyzing multiple online behavioral traces and using sophisticated coordination detection models. By incorporating a control dataset to differentiate organic user activity from coordinated efforts, our findings reveal no evidence of inter-state coordination. These results challenge earlier claims and underscore the importance of robust methodologies and control datasets in accurately detecting online coordination.

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arXiv2025

Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance

Detecting organized political campaigns is of paramount importance in fighting against disinformation on social media. Existing approaches for the identification of such organized actions employ techniques mostly from network science, graph machine learning and natural language processing. Their ultimate goal is to analyze the relationships and interactions (e.g. re-posting) among users and the textual similarities of their posts. Despite their effectiveness in recognizing astroturf campaigns, these methods face significant challenges, notably the class imbalance in available training datasets. To mitigate this issue, recent methods usually resort to data augmentation or increasing the number of positive samples, which may not always be feasible or sufficient in real-world settings. Following a different path, in this paper, we propose a novel framework for identifying astroturf campaigns based solely on large language models (LLMs), introducing a Balanced Retrieval-Augmented Generation (Balanced RAG) component. Our approach first gives both textual information concerning the posts (in our case tweets) and the user interactions of the social network as input to a language model. Then, through prompt engineering and the proposed Balanced RAG method, it effectively detects coordinated disinformation campaigns on X (Twitter). The proposed framework does not require any training or fine-tuning of the language model. Instead, by strategically harnessing the strengths of prompt engineering and Balanced RAG, it facilitates LLMs to overcome the effects of class imbalance and effectively identify coordinated political campaigns. The experimental results demonstrate that by incorporating the proposed prompt engineering and Balanced RAG methods, our framework outperforms the traditional graph-based baselines, achieving 2x-3x improvements in terms of precision, recall and F1 scores.

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arXiv2025

Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure

Misinformation proliferation on social media platforms is a pervasive threat to the integrity of online public discourse. Genuine users, susceptible to others' influence, often unknowingly engage with, endorse, and re-share questionable pieces of information, collectively amplifying the spread of misinformation. In this study, we introduce an empirical framework to investigate users' susceptibility to influence when exposed to unreliable and reliable information sources. Leveraging two datasets on political and public health discussions on Twitter, we analyze the impact of exposure on the adoption of information sources, examining how the reliability of the source modulates this relationship. Our findings provide evidence that increased exposure augments the likelihood of adoption. Users tend to adopt low-credibility sources with fewer exposures than high-credibility sources, a trend that persists even among non-partisan users. Furthermore, the number of exposures needed for adoption varies based on the source credibility, with extreme ends of the spectrum (very high or low credibility) requiring fewer exposures for adoption. Additionally, we reveal that the adoption of information sources often mirrors users' prior exposure to sources with comparable credibility levels. Our research offers critical insights for mitigating the endorsement of misinformation by vulnerable users, offering a framework to study the dynamics of content exposure and adoption on social media platforms.

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arXiv2024

Tracking the 2024 US Presidential Election Chatter on TikTok: A Public Multimodal Dataset

This paper presents the TikTok 2024 U.S. Presidential Election Dataset, a large-scale, resource designed to advance research into political communication and social media dynamics. The dataset comprises 3.14 million videos published on TikTok between November 1, 2023, and October 16, 2024, encompassing video ids and transcripts. Data collection was conducted using the TikTok Research API with a comprehensive set of election-related keywords and hashtags, supplemented by third-party tools to address API limitations and expand content coverage, enabling analysis of hashtag co-occurrence networks that reveal politically aligned hashtags based on ideological affiliations, the evolution of top hashtags over time, and summary statistics that highlight the dataset's scale and richness. This dataset offers insights into TikTok's role in shaping electoral discourse by providing a multimodal view of election-related content. It enables researchers to explore critical topics such as coordinated messaging, misinformation spread, audience engagement, and linguistic trends. The TikTok 2024 U.S. Presidential Election Dataset is publicly available and aims to contribute to the broader understanding of social media's impact on democracy and public opinion.

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arXiv2024

Safe Spaces or Toxic Places? Content Moderation and Social Dynamics of Online Eating Disorder Communities

Social media platforms have become critical spaces for discussing mental health concerns, including eating disorders. While these platforms can provide valuable support networks, they may also amplify harmful content that glorifies disordered cognition and self-destructive behaviors. While social media platforms have implemented various content moderation strategies, from stringent to laissez-faire approaches, we lack a comprehensive understanding of how these different moderation practices interact with user engagement in online communities around these sensitive mental health topics. This study addresses this knowledge gap through a comparative analysis of eating disorder discussions across Twitter/X, Reddit, and TikTok. Our findings reveal that while users across all platforms engage similarly in expressing concerns and seeking support, platforms with weaker moderation (like Twitter/X) enable the formation of toxic echo chambers that amplify pro-anorexia rhetoric. These results demonstrate how moderation strategies significantly influence the development and impact of online communities, particularly in contexts involving mental health and self-harm.

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arXiv2024

Labeled Datasets for Research on Information Operations

Social media platforms have become a hub for political activities and discussions, democratizing participation in these endeavors. However, they have also become an incubator for manipulation campaigns, like information operations (IOs). Some social media platforms have released datasets related to such IOs originating from different countries. However, we lack comprehensive control data that can enable the development of IO detection methods. To bridge this gap, we present new labeled datasets about 26 campaigns, which contain both IO posts verified by a social media platform and over 13M posts by 303k accounts that discussed similar topics in the same time frames (control data). The datasets will facilitate the study of narratives, network interactions, and engagement strategies employed by coordinated accounts across various campaigns and countries. By comparing these coordinated accounts against organic ones, researchers can develop and benchmark IO detection algorithms.

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arXiv2024

Unfiltered Conversations: A Dataset of 2024 U.S. Presidential Election Discourse on Truth Social

Truth Social, launched as a social media platform with a focus on free speech, has become a prominent space for political discourse, attracting a user base with diverse, yet often conservative, viewpoints. As an emerging platform with minimal content moderation, Truth Social has facilitated discussions around contentious social and political issues but has also seen the spread of conspiratorial and hyper-partisan narratives. In this paper, we introduce and release a comprehensive dataset capturing activity on Truth Social related to the upcoming 2024 U.S. Presidential Election, including posts, replies, user interactions, content and media. This dataset comprises 1.5 million posts published between February, 2024 and October 2024, and encompasses key user engagement features and posts metadata. Data collection began in June 2024, though it includes posts published earlier, with the oldest post dating back to February 2022. This offers researchers a unique resource to study communication patterns, the formation of online communities, and the dissemination of information within Truth Social in the run-up to the election. By providing an in-depth view of Truth Social's user dynamics and content distribution, this dataset aims to support further research on political discourse within an alt-tech social media platform. The dataset is publicly available at https://github.com/kashish-s/TruthSocial_2024ElectionInitiative

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arXiv2024

A Public Dataset Tracking Social Media Discourse about the 2024 U.S. Presidential Election on Twitter/X

In this paper, we introduce the first release of a large-scale dataset capturing discourse on $\mathbb{X}$ (a.k.a., Twitter) related to the upcoming 2024 U.S. Presidential Election. Our dataset comprises 22 million publicly available posts on X.com, collected from May 1, 2024, to July 31, 2024, using a custom-built scraper, which we describe in detail. By employing targeted keywords linked to key political figures, events, and emerging issues, we aligned data collection with the election cycle to capture evolving public sentiment and the dynamics of political engagement on social media. This dataset offers researchers a robust foundation to investigate critical questions about the influence of social media in shaping political discourse, the propagation of election-related narratives, and the spread of misinformation. We also present a preliminary analysis that highlights prominent hashtags and keywords within the dataset, offering initial insights into the dominant themes and conversations occurring in the lead-up to the election. Our dataset is available at: url{https://github.com/sinking8/usc-x-24-us-election

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arXiv2024

Unearthing a Billion Telegram Posts about the 2024 U.S. Presidential Election: Development of a Public Dataset

With its lenient moderation policies and long-standing associations with potentially unlawful activities, Telegram has become an incubator for problematic content, frequently featuring conspiratorial, hyper-partisan, and fringe narratives. In the political sphere, these concerns are amplified by reports of Telegram channels being used to organize violent acts, such as those that occurred during the Capitol Hill attack on January 6, 2021. As the 2024 U.S. election approaches, Telegram remains a focal arena for societal and political discourse, warranting close attention from the research community, regulators, and the media. Based on these premises, we introduce and release a Telegram dataset focused on the 2024 U.S. Presidential Election, featuring over 30,000 chats and half a billion messages, including chat details, profile pictures, messages, and user information. We constructed a network of chats and analyzed the 500 most central ones, examining their shared messages. This resource represents the largest public Telegram dataset to date, offering an unprecedented opportunity to study political discussion on Telegram in the lead-up to the 2024 U.S. election. We will continue to collect data until the end of 2024, and routinely update the dataset released at: https://github.com/leonardo-blas/usc-tg-24-us-election

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arXiv2024

Uncovering Coordinated Cross-Platform Information Operations Threatening the Integrity of the 2024 U.S. Presidential Election Online Discussion

Information Operations (IOs) pose a significant threat to the integrity of democratic processes, with the potential to influence election-related online discourse. In anticipation of the 2024 U.S. presidential election, we present a study aimed at uncovering the digital traces of coordinated IOs on $\mathbb{X}$ (formerly Twitter). Using our machine learning framework for detecting online coordination, we analyze a dataset comprising election-related conversations on $\mathbb{X}$ from May 2024. This reveals a network of coordinated inauthentic actors, displaying notable similarities in their link-sharing behaviors. Our analysis shows concerted efforts by these accounts to disseminate misleading, redundant, and biased information across the Web through a coordinated cross-platform information operation: The links shared by this network frequently direct users to other social media platforms or suspicious websites featuring low-quality political content and, in turn, promoting the same $\mathbb{X}$ and YouTube accounts. Members of this network also shared deceptive images generated by AI, accompanied by language attacking political figures and symbolic imagery intended to convey power and dominance. While $\mathbb{X}$ has suspended a subset of these accounts, more than 75% of the coordinated network remains active. Our findings underscore the critical role of developing computational models to scale up the detection of threats on large social media platforms, and emphasize the broader implications of these techniques to detect IOs across the wider Web.

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arXiv2024

The Dawn of Decentralized Social Media: An Exploration of Bluesky's Public Opening

Bluesky is a Twitter-like decentralized social media platform that has recently grown in popularity. After an invite-only period, it opened to the public worldwide on February 6th, 2024. In this paper, we provide a longitudinal analysis of user activity in the two months around the opening, studying changes in the general characteristics of the platform due to the rapid growth of the user base. We observe a broad distribution of activity similar to more established platforms, but a higher volume of original than reshared content, and very low toxicity. After opening to the public, Bluesky experienced a large surge in new users and activity, especially posting English and Japanese content. In particular, several accounts entered the discussion with suspicious behavior, like following many accounts and sharing content from low-credibility news outlets. Some of these have already been classified as spam or suspended, suggesting effective moderation.

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arXiv2024

Toxic Bias: Perspective API Misreads German as More Toxic

Proprietary public APIs play a crucial and growing role as research tools among social scientists. Among such APIs, Google's machine learning-based Perspective API is extensively utilized for assessing the toxicity of social media messages, providing both an important resource for researchers and automatic content moderation. However, this paper exposes an important bias in Perspective API concerning German language text. Through an in-depth examination of several datasets, we uncover intrinsic language biases within the multilingual model of Perspective API. We find that the toxicity assessment of German content produces significantly higher toxicity levels than other languages. This finding is robust across various translations, topics, and data sources, and has significant consequences for both research and moderation strategies that rely on Perspective API. For instance, we show that, on average, four times more tweets and users would be moderated when using the German language compared to their English translation. Our findings point to broader risks associated with the widespread use of proprietary APIs within the computational social sciences.

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arXiv2024

Tracking the 2024 US Presidential Election Chatter on Tiktok: A Public Multimodal Dataset

This paper documents our release of a large-scale data collection of TikTok posts related to the upcoming 2024 U.S. Presidential Election. Our current data comprises 1.8 million videos published between November 1, 2023, and May 26, 2024. Its exploratory analysis identifies the most common keywords, hashtags, and bigrams in both Spanish and English posts, focusing on the election and the two main Presidential candidates, President Joe Biden and Donald Trump. We utilized the TikTok Research API, incorporating various election-related keywords and hashtags, to capture the full scope of relevant content. To address the limitations of the TikTok Research API, we also employed third-party scrapers to expand our dataset. The dataset is publicly available at https://github.com/gabbypinto/US2024PresElectionTikToks

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arXiv2024

Online search is more likely to lead students to validate true news than to refute false ones

With the spread of high-speed Internet and portable smart devices, the way people access and consume information has drastically changed. However, this presents many challenges, including information overload, personal data leakage, and misinformation diffusion. Across the spectrum of risks that Internet users face nowadays, this work focuses on understanding how young people perceive and deal with false information. Within an experimental campaign involving 183 students, we presented six different news items to the participants and invited them to browse the Internet to assess the veracity of the presented information. Our results suggest that online search is more likely to lead students to validate true news than to refute false ones. We found that students change their opinion about a specific piece of information more often than their global idea about a broader topic. Also, our experiment reflected that most participants rely on online sources to obtain information and access the news, and those getting information from books and Internet browsing are the most accurate in assessing the veracity of a news item. This work provides a principled understanding of how young people perceive and distinguish true and false pieces of information, identifying strengths and weaknesses amidst young subjects and contributing to building tailored digital information literacy strategies for youth.

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